Huaiyan Wang

High-precision and resilient indoor positioning is a fundamental requirement for the autonomous operation of mobile robots in GNSS-denied environments. While visual sensors are commonly used for odometry, their operational reliability can be compromised in challenging scenarios such as drastic illumination fluctuations and sparse-textured environments. To address these sensor limitations, this study presents MVO, a magneto-visual odometry framework that explores indoor magnetic field anomalies as complementary constraints for visual odometry. By integrating a 30-magnetometer planar array model with a stereo camera, the proposed system establishes a multi-modal perception framework for indoor spaces. In the frontend, magnetic field gradient information is utilized to provide relative-pose constraints, which assist in the matching of image feature points and help maintain tracking continuity under visual degradation. In the backend, a factor graph optimization (FGO) framework incorporates magnetic relative-pose factors and visual reprojection factors into a unified optimization objective, which is then solved using the incremental smoothing and mapping 2 (iSAM2) algorithm. Frontend-level simulations are conducted to analyze the effects of magnetometer spatial configuration, sensor number, and calibration-error sensitivity on magnetic relative-pose estimation and covariance. Trajectory-level evaluations are further performed on the EuRoC dataset augmented with high-fidelity synthesized magnetic field data, including localization accuracy and computational load. Under this synthesized magnetic field setting, MVO shows improved localization accuracy and moderate computational load compared with the selected MSCKF-Stereo and VINS-Fusion reference baselines. These results provide a simulation-based feasibility validation of integrating magnetic field constraints with visual information for indoor odometry, while validation with real magnetometer array measurements remains future work.

This paper presents a flight control framework based on neural network Model Predictive Control (NN-MPC) to tackle the challenges of acceleration command tracking for supersonic vehicles (SVs) in complex flight environments, addressing the shortcomings of traditional methods in managing nonlinearity, random disturbances, and real-time performance requirements. Initially, a dynamic model is developed through a comprehensive analysis of the vehicle’s dynamic characteristics, incorporating strong cross-coupling effects and disturbance influences. Subsequently, a predictive mechanism is employed to forecast future states and generate virtual control commands, effectively resolving the issue of sluggish responses under rapidly changing commands. Furthermore, the approximation capability of neural networks is leveraged to optimize the control strategy in real time, ensuring that rudder deflection commands adapt to disturbance variations, thus overcoming the robustness limitations inherent in fixed-parameter control approaches. Within the proposed framework, the ultimate uniform bounded stability of the control system is rigorously established using the Lyapunov method. Simulation results demonstrate that the method exhibits exceptional performance under conditions of system state uncertainty and unknown external disturbances, confirming its effectiveness and reliability.

Image matching, which aims to identify reliable correspondences between related images, plays a fundamental and crucial role in many visual tasks. This article focuses on the mismatch removal problem for robust point feature-based matching. First, a robust and effective neighborhood manifold embedding strategy is proposed to preserve the underlying neighborhood topologies of potential true matches. Specifically, we introduce a motion-consistency constraint for neighborhood construction and preserve the manifold of an inlier through finding an optimal subspace embedding fitted by its neighbors. Second, a neighborhood elements ranking consensus for neighborhood refinement is developed to enhance feature matching performance in the presence of high outlier ratios. We term the combination of these two strategies as ranking and neighborhood manifold embedding (RNME), and further formulate the mismatch removal problem into a mathematical optimization model with a closed-form solution. Extensive experiments of general feature matching and image registration on various challenging datasets demonstrate the superiority of RNME over the current state-of-the-art approaches. Moreover, the method also exhibits favorable potential in 3D reconstruction tasks.